Overview

Dataset statistics

Number of variables20
Number of observations19583
Missing cells4240
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory168.0 B

Variable types

Numeric9
Categorical10
Boolean1

Alerts

default is highly imbalanced (53.1%)Imbalance
loan is highly imbalanced (51.1%)Imbalance
poutcome is highly imbalanced (55.9%)Imbalance
age has 211 (1.1%) missing valuesMissing
job has 236 (1.2%) missing valuesMissing
marital has 212 (1.1%) missing valuesMissing
education has 220 (1.1%) missing valuesMissing
default has 200 (1.0%) missing valuesMissing
loan has 240 (1.2%) missing valuesMissing
contact has 206 (1.1%) missing valuesMissing
month has 212 (1.1%) missing valuesMissing
day_of_week has 212 (1.1%) missing valuesMissing
campaign has 213 (1.1%) missing valuesMissing
pdays has 237 (1.2%) missing valuesMissing
poutcome has 231 (1.2%) missing valuesMissing
emp.var.rate has 205 (1.0%) missing valuesMissing
cons.conf.idx has 212 (1.1%) missing valuesMissing
euribor3m has 210 (1.1%) missing valuesMissing
nr.employed has 241 (1.2%) missing valuesMissing
previous has 16665 (85.1%) zerosZeros

Reproduction

Analysis started2024-03-03 17:53:14.836057
Analysis finished2024-03-03 17:53:41.102859
Duration26.27 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

age
Real number (ℝ)

MISSING 

Distinct76
Distinct (%)0.4%
Missing211
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean40.075573
Minimum17
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.0 KiB
2024-03-03T17:53:41.312733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum95
Range78
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.380931
Coefficient of variation (CV)0.25903389
Kurtosis0.71465246
Mean40.075573
Median Absolute Deviation (MAD)7
Skewness0.76771163
Sum776344
Variance107.76374
MonotonicityNot monotonic
2024-03-03T17:53:41.761638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 923
 
4.7%
32 873
 
4.5%
36 852
 
4.4%
33 845
 
4.3%
35 822
 
4.2%
34 802
 
4.1%
30 778
 
4.0%
37 725
 
3.7%
39 667
 
3.4%
29 665
 
3.4%
Other values (66) 11420
58.3%
ValueCountFrequency (%)
17 2
 
< 0.1%
18 10
 
0.1%
19 20
 
0.1%
20 32
 
0.2%
21 39
 
0.2%
22 68
 
0.3%
23 103
 
0.5%
24 210
1.1%
25 269
1.4%
26 334
1.7%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 1
 
< 0.1%
92 1
 
< 0.1%
91 2
 
< 0.1%
89 2
 
< 0.1%
88 8
< 0.1%
87 1
 
< 0.1%
85 3
 
< 0.1%
84 4
< 0.1%
83 7
< 0.1%

job
Categorical

MISSING 

Distinct12
Distinct (%)0.1%
Missing236
Missing (%)1.2%
Memory size306.0 KiB
admin.
4877 
blue-collar
4364 
technician
3125 
services
1856 
management
1395 
Other values (7)
3730 

Length

Max length13
Median length12
Mean length8.9558588
Min length6

Characters and Unicode

Total characters173269
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowentrepreneur
2nd rowtechnician
3rd rowadmin.
4th rowtechnician
5th rowstudent

Common Values

ValueCountFrequency (%)
admin. 4877
24.9%
blue-collar 4364
22.3%
technician 3125
16.0%
services 1856
 
9.5%
management 1395
 
7.1%
retired 828
 
4.2%
entrepreneur 674
 
3.4%
self-employed 665
 
3.4%
housemaid 503
 
2.6%
unemployed 493
 
2.5%
Other values (2) 567
 
2.9%

Length

2024-03-03T17:53:42.260816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 4877
25.2%
blue-collar 4364
22.6%
technician 3125
16.2%
services 1856
 
9.6%
management 1395
 
7.2%
retired 828
 
4.3%
entrepreneur 674
 
3.5%
self-employed 665
 
3.4%
housemaid 503
 
2.6%
unemployed 493
 
2.5%
Other values (2) 567
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 22229
12.8%
n 16655
 
9.6%
a 15659
 
9.0%
l 14915
 
8.6%
i 14314
 
8.3%
c 12470
 
7.2%
r 9898
 
5.7%
m 9328
 
5.4%
d 7768
 
4.5%
t 6826
 
3.9%
Other values (14) 43207
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 163363
94.3%
Dash Punctuation 5029
 
2.9%
Other Punctuation 4877
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22229
13.6%
n 16655
10.2%
a 15659
9.6%
l 14915
9.1%
i 14314
8.8%
c 12470
 
7.6%
r 9898
 
6.1%
m 9328
 
5.7%
d 7768
 
4.8%
t 6826
 
4.2%
Other values (12) 33301
20.4%
Dash Punctuation
ValueCountFrequency (%)
- 5029
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4877
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 163363
94.3%
Common 9906
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22229
13.6%
n 16655
10.2%
a 15659
9.6%
l 14915
9.1%
i 14314
8.8%
c 12470
 
7.6%
r 9898
 
6.1%
m 9328
 
5.7%
d 7768
 
4.8%
t 6826
 
4.2%
Other values (12) 33301
20.4%
Common
ValueCountFrequency (%)
- 5029
50.8%
. 4877
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 22229
12.8%
n 16655
 
9.6%
a 15659
 
9.0%
l 14915
 
8.6%
i 14314
 
8.3%
c 12470
 
7.2%
r 9898
 
5.7%
m 9328
 
5.4%
d 7768
 
4.5%
t 6826
 
3.9%
Other values (14) 43207
24.9%

marital
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing212
Missing (%)1.1%
Memory size306.0 KiB
married
11739 
single
5398 
divorced
2189 
unknown
 
45

Length

Max length8
Median length7
Mean length6.83434
Min length6

Characters and Unicode

Total characters132388
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsingle
2nd rowmarried
3rd rowmarried
4th rowsingle
5th rowsingle

Common Values

ValueCountFrequency (%)
married 11739
59.9%
single 5398
27.6%
divorced 2189
 
11.2%
unknown 45
 
0.2%
(Missing) 212
 
1.1%

Length

2024-03-03T17:53:42.675950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T17:53:43.001898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
married 11739
60.6%
single 5398
27.9%
divorced 2189
 
11.3%
unknown 45
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 25667
19.4%
i 19326
14.6%
e 19326
14.6%
d 16117
12.2%
m 11739
8.9%
a 11739
8.9%
n 5533
 
4.2%
s 5398
 
4.1%
g 5398
 
4.1%
l 5398
 
4.1%
Other values (6) 6747
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 132388
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 25667
19.4%
i 19326
14.6%
e 19326
14.6%
d 16117
12.2%
m 11739
8.9%
a 11739
8.9%
n 5533
 
4.2%
s 5398
 
4.1%
g 5398
 
4.1%
l 5398
 
4.1%
Other values (6) 6747
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 132388
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 25667
19.4%
i 19326
14.6%
e 19326
14.6%
d 16117
12.2%
m 11739
8.9%
a 11739
8.9%
n 5533
 
4.2%
s 5398
 
4.1%
g 5398
 
4.1%
l 5398
 
4.1%
Other values (6) 6747
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 25667
19.4%
i 19326
14.6%
e 19326
14.6%
d 16117
12.2%
m 11739
8.9%
a 11739
8.9%
n 5533
 
4.2%
s 5398
 
4.1%
g 5398
 
4.1%
l 5398
 
4.1%
Other values (6) 6747
 
5.1%

education
Categorical

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing220
Missing (%)1.1%
Memory size306.0 KiB
university.degree
5703 
high.school
4437 
basic.9y
2895 
professional.course
2443 
basic.4y
1948 
Other values (3)
1937 

Length

Max length19
Median length17
Mean length12.68476
Min length7

Characters and Unicode

Total characters245615
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.9y
2nd rowprofessional.course
3rd rowunknown
4th rowprofessional.course
5th rowunknown

Common Values

ValueCountFrequency (%)
university.degree 5703
29.1%
high.school 4437
22.7%
basic.9y 2895
14.8%
professional.course 2443
12.5%
basic.4y 1948
 
9.9%
basic.6y 1110
 
5.7%
unknown 818
 
4.2%
illiterate 9
 
< 0.1%
(Missing) 220
 
1.1%

Length

2024-03-03T17:53:43.360893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T17:53:43.679839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 5703
29.5%
high.school 4437
22.9%
basic.9y 2895
15.0%
professional.course 2443
12.6%
basic.4y 1948
 
10.1%
basic.6y 1110
 
5.7%
unknown 818
 
4.2%
illiterate 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 27716
 
11.3%
i 24257
 
9.9%
s 23422
 
9.5%
. 18536
 
7.5%
o 17021
 
6.9%
r 16301
 
6.6%
h 13311
 
5.4%
c 12833
 
5.2%
y 11656
 
4.7%
n 10600
 
4.3%
Other values (15) 69962
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 221126
90.0%
Other Punctuation 18536
 
7.5%
Decimal Number 5953
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 27716
12.5%
i 24257
11.0%
s 23422
10.6%
o 17021
 
7.7%
r 16301
 
7.4%
h 13311
 
6.0%
c 12833
 
5.8%
y 11656
 
5.3%
n 10600
 
4.8%
g 10140
 
4.6%
Other values (11) 53869
24.4%
Decimal Number
ValueCountFrequency (%)
9 2895
48.6%
4 1948
32.7%
6 1110
 
18.6%
Other Punctuation
ValueCountFrequency (%)
. 18536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 221126
90.0%
Common 24489
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 27716
12.5%
i 24257
11.0%
s 23422
10.6%
o 17021
 
7.7%
r 16301
 
7.4%
h 13311
 
6.0%
c 12833
 
5.8%
y 11656
 
5.3%
n 10600
 
4.8%
g 10140
 
4.6%
Other values (11) 53869
24.4%
Common
ValueCountFrequency (%)
. 18536
75.7%
9 2895
 
11.8%
4 1948
 
8.0%
6 1110
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 245615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 27716
 
11.3%
i 24257
 
9.9%
s 23422
 
9.5%
. 18536
 
7.5%
o 17021
 
6.9%
r 16301
 
6.6%
h 13311
 
5.4%
c 12833
 
5.2%
y 11656
 
4.7%
n 10600
 
4.3%
Other values (15) 69962
28.5%

default
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing200
Missing (%)1.0%
Memory size306.0 KiB
no
15306 
unknown
4074 
yes
 
3

Length

Max length7
Median length2
Mean length3.0510757
Min length2

Characters and Unicode

Total characters59139
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 15306
78.2%
unknown 4074
 
20.8%
yes 3
 
< 0.1%
(Missing) 200
 
1.0%

Length

2024-03-03T17:53:44.093232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T17:53:44.382445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no 15306
79.0%
unknown 4074
 
21.0%
yes 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 27528
46.5%
o 19380
32.8%
u 4074
 
6.9%
k 4074
 
6.9%
w 4074
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 59139
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 27528
46.5%
o 19380
32.8%
u 4074
 
6.9%
k 4074
 
6.9%
w 4074
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 59139
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 27528
46.5%
o 19380
32.8%
u 4074
 
6.9%
k 4074
 
6.9%
w 4074
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 27528
46.5%
o 19380
32.8%
u 4074
 
6.9%
k 4074
 
6.9%
w 4074
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

housing
Categorical

Distinct3
Distinct (%)< 0.1%
Missing188
Missing (%)1.0%
Memory size306.0 KiB
yes
10151 
no
8767 
unknown
 
477

Length

Max length7
Median length3
Mean length2.6463522
Min length2

Characters and Unicode

Total characters51326
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowyes
3rd rowyes
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
yes 10151
51.8%
no 8767
44.8%
unknown 477
 
2.4%
(Missing) 188
 
1.0%

Length

2024-03-03T17:53:44.732943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T17:53:45.058767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 10151
52.3%
no 8767
45.2%
unknown 477
 
2.5%

Most occurring characters

ValueCountFrequency (%)
n 10198
19.9%
y 10151
19.8%
e 10151
19.8%
s 10151
19.8%
o 9244
18.0%
u 477
 
0.9%
k 477
 
0.9%
w 477
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51326
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 10198
19.9%
y 10151
19.8%
e 10151
19.8%
s 10151
19.8%
o 9244
18.0%
u 477
 
0.9%
k 477
 
0.9%
w 477
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 51326
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 10198
19.9%
y 10151
19.8%
e 10151
19.8%
s 10151
19.8%
o 9244
18.0%
u 477
 
0.9%
k 477
 
0.9%
w 477
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 10198
19.9%
y 10151
19.8%
e 10151
19.8%
s 10151
19.8%
o 9244
18.0%
u 477
 
0.9%
k 477
 
0.9%
w 477
 
0.9%

loan
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing240
Missing (%)1.2%
Memory size306.0 KiB
no
15921 
yes
2949 
unknown
 
473

Length

Max length7
Median length2
Mean length2.2747247
Min length2

Characters and Unicode

Total characters44000
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 15921
81.3%
yes 2949
 
15.1%
unknown 473
 
2.4%
(Missing) 240
 
1.2%

Length

2024-03-03T17:53:45.425462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T17:53:45.722519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no 15921
82.3%
yes 2949
 
15.2%
unknown 473
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 17340
39.4%
o 16394
37.3%
y 2949
 
6.7%
e 2949
 
6.7%
s 2949
 
6.7%
u 473
 
1.1%
k 473
 
1.1%
w 473
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 17340
39.4%
o 16394
37.3%
y 2949
 
6.7%
e 2949
 
6.7%
s 2949
 
6.7%
u 473
 
1.1%
k 473
 
1.1%
w 473
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 44000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 17340
39.4%
o 16394
37.3%
y 2949
 
6.7%
e 2949
 
6.7%
s 2949
 
6.7%
u 473
 
1.1%
k 473
 
1.1%
w 473
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 17340
39.4%
o 16394
37.3%
y 2949
 
6.7%
e 2949
 
6.7%
s 2949
 
6.7%
u 473
 
1.1%
k 473
 
1.1%
w 473
 
1.1%

contact
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing206
Missing (%)1.1%
Memory size306.0 KiB
cellular
12232 
telephone
7145 

Length

Max length9
Median length8
Mean length8.3687361
Min length8

Characters and Unicode

Total characters162161
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcellular
2nd rowcellular
3rd rowcellular
4th rowtelephone
5th rowcellular

Common Values

ValueCountFrequency (%)
cellular 12232
62.5%
telephone 7145
36.5%
(Missing) 206
 
1.1%

Length

2024-03-03T17:53:46.062837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T17:53:46.358659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular 12232
63.1%
telephone 7145
36.9%

Most occurring characters

ValueCountFrequency (%)
l 43841
27.0%
e 33667
20.8%
c 12232
 
7.5%
u 12232
 
7.5%
a 12232
 
7.5%
r 12232
 
7.5%
t 7145
 
4.4%
p 7145
 
4.4%
h 7145
 
4.4%
o 7145
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 162161
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 43841
27.0%
e 33667
20.8%
c 12232
 
7.5%
u 12232
 
7.5%
a 12232
 
7.5%
r 12232
 
7.5%
t 7145
 
4.4%
p 7145
 
4.4%
h 7145
 
4.4%
o 7145
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 162161
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 43841
27.0%
e 33667
20.8%
c 12232
 
7.5%
u 12232
 
7.5%
a 12232
 
7.5%
r 12232
 
7.5%
t 7145
 
4.4%
p 7145
 
4.4%
h 7145
 
4.4%
o 7145
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 43841
27.0%
e 33667
20.8%
c 12232
 
7.5%
u 12232
 
7.5%
a 12232
 
7.5%
r 12232
 
7.5%
t 7145
 
4.4%
p 7145
 
4.4%
h 7145
 
4.4%
o 7145
 
4.4%

month
Categorical

MISSING 

Distinct10
Distinct (%)0.1%
Missing212
Missing (%)1.1%
Memory size306.0 KiB
may
6426 
jul
3358 
aug
2907 
jun
2514 
nov
1903 
Other values (5)
2263 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58113
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowjul
2nd rowmay
3rd rowaug
4th rowjun
5th rowapr

Common Values

ValueCountFrequency (%)
may 6426
32.8%
jul 3358
17.1%
aug 2907
14.8%
jun 2514
 
12.8%
nov 1903
 
9.7%
apr 1288
 
6.6%
oct 365
 
1.9%
sep 258
 
1.3%
mar 256
 
1.3%
dec 96
 
0.5%
(Missing) 212
 
1.1%

Length

2024-03-03T17:53:46.663518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T17:53:47.007854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
may 6426
33.2%
jul 3358
17.3%
aug 2907
15.0%
jun 2514
 
13.0%
nov 1903
 
9.8%
apr 1288
 
6.6%
oct 365
 
1.9%
sep 258
 
1.3%
mar 256
 
1.3%
dec 96
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a 10877
18.7%
u 8779
15.1%
m 6682
11.5%
y 6426
11.1%
j 5872
10.1%
n 4417
7.6%
l 3358
 
5.8%
g 2907
 
5.0%
o 2268
 
3.9%
v 1903
 
3.3%
Other values (7) 4624
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58113
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10877
18.7%
u 8779
15.1%
m 6682
11.5%
y 6426
11.1%
j 5872
10.1%
n 4417
7.6%
l 3358
 
5.8%
g 2907
 
5.0%
o 2268
 
3.9%
v 1903
 
3.3%
Other values (7) 4624
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58113
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10877
18.7%
u 8779
15.1%
m 6682
11.5%
y 6426
11.1%
j 5872
10.1%
n 4417
7.6%
l 3358
 
5.8%
g 2907
 
5.0%
o 2268
 
3.9%
v 1903
 
3.3%
Other values (7) 4624
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10877
18.7%
u 8779
15.1%
m 6682
11.5%
y 6426
11.1%
j 5872
10.1%
n 4417
7.6%
l 3358
 
5.8%
g 2907
 
5.0%
o 2268
 
3.9%
v 1903
 
3.3%
Other values (7) 4624
8.0%

day_of_week
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing212
Missing (%)1.1%
Memory size306.0 KiB
thu
4058 
mon
4054 
wed
3814 
tue
3755 
fri
3690 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58113
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowtue
3rd rowmon
4th rowtue
5th rowwed

Common Values

ValueCountFrequency (%)
thu 4058
20.7%
mon 4054
20.7%
wed 3814
19.5%
tue 3755
19.2%
fri 3690
18.8%
(Missing) 212
 
1.1%

Length

2024-03-03T17:53:47.375048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T17:53:47.691897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
thu 4058
20.9%
mon 4054
20.9%
wed 3814
19.7%
tue 3755
19.4%
fri 3690
19.0%

Most occurring characters

ValueCountFrequency (%)
t 7813
13.4%
u 7813
13.4%
e 7569
13.0%
h 4058
7.0%
m 4054
7.0%
o 4054
7.0%
n 4054
7.0%
w 3814
6.6%
d 3814
6.6%
f 3690
6.3%
Other values (2) 7380
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58113
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 7813
13.4%
u 7813
13.4%
e 7569
13.0%
h 4058
7.0%
m 4054
7.0%
o 4054
7.0%
n 4054
7.0%
w 3814
6.6%
d 3814
6.6%
f 3690
6.3%
Other values (2) 7380
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 58113
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 7813
13.4%
u 7813
13.4%
e 7569
13.0%
h 4058
7.0%
m 4054
7.0%
o 4054
7.0%
n 4054
7.0%
w 3814
6.6%
d 3814
6.6%
f 3690
6.3%
Other values (2) 7380
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 7813
13.4%
u 7813
13.4%
e 7569
13.0%
h 4058
7.0%
m 4054
7.0%
o 4054
7.0%
n 4054
7.0%
w 3814
6.6%
d 3814
6.6%
f 3690
6.3%
Other values (2) 7380
12.7%

campaign
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)0.2%
Missing213
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean2.6340733
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.0 KiB
2024-03-03T17:53:48.015914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8993147
Coefficient of variation (CV)1.1006963
Kurtosis37.354734
Mean2.6340733
Median Absolute Deviation (MAD)1
Skewness4.8066441
Sum51022
Variance8.4060257
MonotonicityNot monotonic
2024-03-03T17:53:48.391503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 8071
41.2%
2 4997
25.5%
3 2603
 
13.3%
4 1266
 
6.5%
5 785
 
4.0%
6 479
 
2.4%
7 272
 
1.4%
8 190
 
1.0%
9 146
 
0.7%
10 103
 
0.5%
Other values (29) 458
 
2.3%
(Missing) 213
 
1.1%
ValueCountFrequency (%)
1 8071
41.2%
2 4997
25.5%
3 2603
 
13.3%
4 1266
 
6.5%
5 785
 
4.0%
6 479
 
2.4%
7 272
 
1.4%
8 190
 
1.0%
9 146
 
0.7%
10 103
 
0.5%
ValueCountFrequency (%)
56 1
 
< 0.1%
43 2
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
35 3
< 0.1%
34 2
 
< 0.1%
33 2
 
< 0.1%
32 2
 
< 0.1%
31 5
< 0.1%
30 2
 
< 0.1%

pdays
Real number (ℝ)

MISSING 

Distinct25
Distinct (%)0.1%
Missing237
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean961.22506
Minimum0
Maximum999
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size306.0 KiB
2024-03-03T17:53:48.739913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation189.95584
Coefficient of variation (CV)0.19761848
Kurtosis21.33228
Mean961.22506
Median Absolute Deviation (MAD)0
Skewness-4.8300784
Sum18595860
Variance36083.221
MonotonicityNot monotonic
2024-03-03T17:53:49.073602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
999 18610
95.0%
6 214
 
1.1%
3 206
 
1.1%
4 50
 
0.3%
9 36
 
0.2%
7 32
 
0.2%
5 25
 
0.1%
2 25
 
0.1%
13 23
 
0.1%
10 19
 
0.1%
Other values (15) 106
 
0.5%
(Missing) 237
 
1.2%
ValueCountFrequency (%)
0 8
 
< 0.1%
1 13
 
0.1%
2 25
 
0.1%
3 206
1.1%
4 50
 
0.3%
5 25
 
0.1%
6 214
1.1%
7 32
 
0.2%
8 11
 
0.1%
9 36
 
0.2%
ValueCountFrequency (%)
999 18610
95.0%
26 1
 
< 0.1%
25 1
 
< 0.1%
22 2
 
< 0.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
18 4
 
< 0.1%
17 5
 
< 0.1%
16 5
 
< 0.1%
15 11
 
0.1%

previous
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing187
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.17952155
Minimum0
Maximum7
Zeros16665
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size306.0 KiB
2024-03-03T17:53:49.346805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.50666636
Coefficient of variation (CV)2.822315
Kurtosis19.63805
Mean0.17952155
Median Absolute Deviation (MAD)0
Skewness3.7933634
Sum3482
Variance0.2567108
MonotonicityNot monotonic
2024-03-03T17:53:50.005391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 16665
85.1%
1 2206
 
11.3%
2 363
 
1.9%
3 114
 
0.6%
4 35
 
0.2%
5 11
 
0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
(Missing) 187
 
1.0%
ValueCountFrequency (%)
0 16665
85.1%
1 2206
 
11.3%
2 363
 
1.9%
3 114
 
0.6%
4 35
 
0.2%
5 11
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 1
 
< 0.1%
5 11
 
0.1%
4 35
 
0.2%
3 114
 
0.6%
2 363
 
1.9%
1 2206
 
11.3%
0 16665
85.1%

poutcome
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing231
Missing (%)1.2%
Memory size306.0 KiB
nonexistent
16627 
failure
2059 
success
 
666

Length

Max length11
Median length11
Mean length10.436751
Min length7

Characters and Unicode

Total characters201972
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 16627
84.9%
failure 2059
 
10.5%
success 666
 
3.4%
(Missing) 231
 
1.2%

Length

2024-03-03T17:53:50.329483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T17:53:50.636672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 16627
85.9%
failure 2059
 
10.6%
success 666
 
3.4%

Most occurring characters

ValueCountFrequency (%)
n 49881
24.7%
e 35979
17.8%
t 33254
16.5%
i 18686
 
9.3%
s 18625
 
9.2%
x 16627
 
8.2%
o 16627
 
8.2%
u 2725
 
1.3%
f 2059
 
1.0%
a 2059
 
1.0%
Other values (3) 5450
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 201972
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 49881
24.7%
e 35979
17.8%
t 33254
16.5%
i 18686
 
9.3%
s 18625
 
9.2%
x 16627
 
8.2%
o 16627
 
8.2%
u 2725
 
1.3%
f 2059
 
1.0%
a 2059
 
1.0%
Other values (3) 5450
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 201972
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 49881
24.7%
e 35979
17.8%
t 33254
16.5%
i 18686
 
9.3%
s 18625
 
9.2%
x 16627
 
8.2%
o 16627
 
8.2%
u 2725
 
1.3%
f 2059
 
1.0%
a 2059
 
1.0%
Other values (3) 5450
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 49881
24.7%
e 35979
17.8%
t 33254
16.5%
i 18686
 
9.3%
s 18625
 
9.2%
x 16627
 
8.2%
o 16627
 
8.2%
u 2725
 
1.3%
f 2059
 
1.0%
a 2059
 
1.0%
Other values (3) 5450
 
2.7%

emp.var.rate
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)0.1%
Missing205
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.063314067
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative8175
Negative (%)41.7%
Memory size306.0 KiB
2024-03-03T17:53:50.930131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5798793
Coefficient of variation (CV)24.953053
Kurtosis-1.0986502
Mean0.063314067
Median Absolute Deviation (MAD)0.3
Skewness-0.70166552
Sum1226.9
Variance2.4960185
MonotonicityNot monotonic
2024-03-03T17:53:51.220325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 7607
38.8%
-1.8 4385
22.4%
1.1 3596
18.4%
-0.1 1702
 
8.7%
-2.9 795
 
4.1%
-3.4 520
 
2.7%
-1.7 375
 
1.9%
-1.1 303
 
1.5%
-3 90
 
0.5%
-0.2 5
 
< 0.1%
(Missing) 205
 
1.0%
ValueCountFrequency (%)
-3.4 520
 
2.7%
-3 90
 
0.5%
-2.9 795
 
4.1%
-1.8 4385
22.4%
-1.7 375
 
1.9%
-1.1 303
 
1.5%
-0.2 5
 
< 0.1%
-0.1 1702
 
8.7%
1.1 3596
18.4%
1.4 7607
38.8%
ValueCountFrequency (%)
1.4 7607
38.8%
1.1 3596
18.4%
-0.1 1702
 
8.7%
-0.2 5
 
< 0.1%
-1.1 303
 
1.5%
-1.7 375
 
1.9%
-1.8 4385
22.4%
-2.9 795
 
4.1%
-3 90
 
0.5%
-3.4 520
 
2.7%

cons.price.idx
Real number (ℝ)

Distinct26
Distinct (%)0.1%
Missing187
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean93.571933
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.0 KiB
2024-03-03T17:53:51.510941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.649
Q193.075
median93.444
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.58301992
Coefficient of variation (CV)0.0062307137
Kurtosis-0.8372412
Mean93.571933
Median Absolute Deviation (MAD)0.55
Skewness-0.22587736
Sum1814921.2
Variance0.33991223
MonotonicityNot monotonic
2024-03-03T17:53:51.856800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 3596
18.4%
93.918 3125
16.0%
92.893 2751
14.0%
93.444 2415
12.3%
94.465 2080
10.6%
93.2 1661
8.5%
93.075 1202
 
6.1%
92.201 388
 
2.0%
92.963 331
 
1.7%
92.431 223
 
1.1%
Other values (16) 1624
8.3%
(Missing) 187
 
1.0%
ValueCountFrequency (%)
92.201 388
 
2.0%
92.379 121
 
0.6%
92.431 223
 
1.1%
92.469 79
 
0.4%
92.649 177
 
0.9%
92.713 91
 
0.5%
92.756 5
 
< 0.1%
92.843 133
 
0.7%
92.893 2751
14.0%
92.963 331
 
1.7%
ValueCountFrequency (%)
94.767 61
 
0.3%
94.601 103
 
0.5%
94.465 2080
10.6%
94.215 159
 
0.8%
94.199 139
 
0.7%
94.055 101
 
0.5%
94.027 112
 
0.6%
93.994 3596
18.4%
93.918 3125
16.0%
93.876 100
 
0.5%

cons.conf.idx
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)0.1%
Missing212
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean-40.518146
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative19371
Negative (%)98.9%
Memory size306.0 KiB
2024-03-03T17:53:52.182967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.6646693
Coefficient of variation (CV)-0.11512544
Kurtosis-0.35606586
Mean-40.518146
Median Absolute Deviation (MAD)4.4
Skewness0.31822053
Sum-784877
Variance21.759139
MonotonicityNot monotonic
2024-03-03T17:53:52.527030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 3599
18.4%
-42.7 3122
15.9%
-46.2 2747
14.0%
-36.1 2406
12.3%
-41.8 2078
10.6%
-42 1668
8.5%
-47.1 1199
 
6.1%
-31.4 388
 
2.0%
-40.8 325
 
1.7%
-26.9 220
 
1.1%
Other values (16) 1619
8.3%
(Missing) 212
 
1.1%
ValueCountFrequency (%)
-50.8 62
 
0.3%
-50 132
 
0.7%
-49.5 103
 
0.5%
-47.1 1199
 
6.1%
-46.2 2747
14.0%
-45.9 5
 
< 0.1%
-42.7 3122
15.9%
-42 1668
8.5%
-41.8 2078
10.6%
-40.8 325
 
1.7%
ValueCountFrequency (%)
-26.9 220
 
1.1%
-29.8 120
 
0.6%
-30.1 179
 
0.9%
-31.4 388
 
2.0%
-33 90
 
0.5%
-33.6 80
 
0.4%
-34.6 85
 
0.4%
-34.8 122
 
0.6%
-36.1 2406
12.3%
-36.4 3599
18.4%

euribor3m
Real number (ℝ)

MISSING 

Distinct302
Distinct (%)1.6%
Missing210
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean3.5981378
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.0 KiB
2024-03-03T17:53:52.877507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.781
Q11.334
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.627

Descriptive statistics

Standard deviation1.7446063
Coefficient of variation (CV)0.48486366
Kurtosis-1.4492986
Mean3.5981378
Median Absolute Deviation (MAD)0.108
Skewness-0.68021649
Sum69706.724
Variance3.0436511
MonotonicityNot monotonic
2024-03-03T17:53:53.259745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 1329
 
6.8%
4.962 1208
 
6.2%
4.963 1170
 
6.0%
4.961 916
 
4.7%
1.405 579
 
3.0%
4.964 549
 
2.8%
4.856 542
 
2.8%
4.965 513
 
2.6%
4.96 498
 
2.5%
4.864 455
 
2.3%
Other values (292) 11614
59.3%
ValueCountFrequency (%)
0.634 3
 
< 0.1%
0.635 22
0.1%
0.636 6
 
< 0.1%
0.637 3
 
< 0.1%
0.638 1
 
< 0.1%
0.639 10
0.1%
0.64 5
 
< 0.1%
0.642 20
0.1%
0.643 14
0.1%
0.644 15
0.1%
ValueCountFrequency (%)
5.045 1
 
< 0.1%
5 6
 
< 0.1%
4.97 75
 
0.4%
4.968 454
 
2.3%
4.967 301
 
1.5%
4.966 287
 
1.5%
4.965 513
2.6%
4.964 549
2.8%
4.963 1170
6.0%
4.962 1208
6.2%

nr.employed
Real number (ℝ)

MISSING 

Distinct11
Distinct (%)0.1%
Missing241
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean5166.241
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.0 KiB
2024-03-03T17:53:53.572427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5008.7
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.662371
Coefficient of variation (CV)0.014064843
Kurtosis-0.063106789
Mean5166.241
Median Absolute Deviation (MAD)37.1
Skewness-1.0201209
Sum99925434
Variance5279.8201
MonotonicityNot monotonic
2024-03-03T17:53:53.868214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 7592
38.8%
5099.1 4077
20.8%
5191 3599
18.4%
5195.8 1693
 
8.6%
5076.2 790
 
4.0%
5017.5 520
 
2.7%
4991.6 367
 
1.9%
4963.6 305
 
1.6%
5008.7 303
 
1.5%
5023.5 91
 
0.5%
(Missing) 241
 
1.2%
ValueCountFrequency (%)
4963.6 305
 
1.6%
4991.6 367
 
1.9%
5008.7 303
 
1.5%
5017.5 520
 
2.7%
5023.5 91
 
0.5%
5076.2 790
 
4.0%
5099.1 4077
20.8%
5176.3 5
 
< 0.1%
5191 3599
18.4%
5195.8 1693
8.6%
ValueCountFrequency (%)
5228.1 7592
38.8%
5195.8 1693
 
8.6%
5191 3599
18.4%
5176.3 5
 
< 0.1%
5099.1 4077
20.8%
5076.2 790
 
4.0%
5023.5 91
 
0.5%
5017.5 520
 
2.7%
5008.7 303
 
1.5%
4991.6 367
 
1.9%

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing180
Missing (%)0.9%
Memory size191.2 KiB
False
17138 
True
2265 
(Missing)
 
180
ValueCountFrequency (%)
False 17138
87.5%
True 2265
 
11.6%
(Missing) 180
 
0.9%
2024-03-03T17:53:54.180202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Interactions

2024-03-03T17:53:37.042867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:17.948305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:20.515299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:23.114345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:25.310776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:27.691588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:29.931501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:32.132801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:34.713170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:37.304909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:18.254685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:20.779059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:23.366607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:25.587871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:27.936757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:30.173880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:32.395970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:35.049731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:37.560555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:18.584144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:21.054548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:23.594427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:25.838151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:28.199229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:30.415439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:32.668750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:35.297786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:37.809190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:18.927835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:21.329188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:23.853113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:26.115648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:28.450329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:30.648863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:32.919157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:35.535206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:38.078083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:19.220865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:21.590994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:24.106721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:26.399983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:28.713776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:30.914089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:33.170299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:35.800146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:38.318269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:19.469482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:21.832872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:24.366524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:26.657862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:28.949538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:31.162591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:33.411457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:36.049137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:38.567537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:19.739158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:22.075360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:24.606111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:26.927028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:29.193824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:31.400873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:33.648286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:36.338740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:38.803782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:19.998206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:22.346332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:24.844481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:27.178293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:29.429398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:31.651740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:33.885434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:36.577284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:39.098923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:20.252228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:22.859604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:25.074312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:27.448239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:29.656665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:31.883635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:34.105686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T17:53:36.813493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-03-03T17:53:39.493733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-03T17:53:40.310095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
1439029.0entrepreneursinglebasic.9ynononocellularjulmon3.0999.00.0nonexistent1.493.918-42.74.9625228.1no
3355031.0technicianmarriedprofessional.coursenoyesnocellularmaytue4.0999.00.0nonexistent-1.892.893-46.21.2915099.1no
2285756.0admin.marriedunknownnoyesnocellularaugmon2.0999.00.0nonexistent1.493.444-36.14.9655228.1no
1069547.0technicianNaNprofessional.coursenoyesnotelephonejuntue6.0999.00.0nonexistent1.494.465-41.84.9615228.1no
3004230.0studentsingleunknownnoyesnocellularaprwed1.0999.00.0nonexistent-1.893.075-47.11.4055099.1no
2589629.0techniciansingleprofessional.coursenoyesnocellularnovwed3.0999.00.0nonexistent-0.193.200-42.04.120NaNno
898450.0technicianmarriedprofessional.coursenononotelephonejunthu1.0999.00.0nonexistent1.494.465-41.8NaN5228.1no
245828.0admin.marrieduniversity.degreenounknownunknowntelephonemaytue2.0999.00.0nonexistent1.193.994-36.44.8565191.0no
2278760.0blue-collarmarriedunknownnononocellularaugmon3.0999.00.0nonexistent1.493.444-36.14.9655228.1no
3174026.0blue-collarsinglebasic.4ynonoyescellularmaythu1.0999.01.0failure-1.892.893-46.21.3275099.1no
agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
3057835.0housemaidsingleuniversity.degreenonoyescellularmaymon4.0999.00.0nonexistent-1.892.893-46.21.3545099.1no
3430726.0managementmarrieduniversity.degreenononocellularmayNaN4.0999.00.0nonexistent-1.892.893-46.21.2665099.1no
2463735.0NaNmarrieduniversity.degreenoyesnocellularnovmon2.0999.00.0nonexistent-0.1NaN-42.04.191NaNno
2593330.0technicianmarriedhigh.schoolnononocellularnovwed2.0999.00.0nonexistent-0.193.200-42.04.1205195.8no
3926180.0retiredmarriedbasic.4ynononocellularmarfri3.06.04.0success-1.893.369-34.80.6495008.7yes
3886335.0managementmarrieduniversity.degreeNaNnonotelephonenovmon1.07.03.0failure-3.492.649-30.10.7145017.5no
3573224.0blue-collarsinglebasic.9yNaNyesnocellularmaymon3.0999.00.0nonexistent-1.892.893-46.21.2445099.1no
2799129.0admin.singleuniversity.degreenoyesnocellularaprwed1.05.02.0success-1.893.075-47.11.4985099.1no
3887877.0retireddivorcedNaNnoyesnocellularnovmon2.0999.01.0failure-3.492.649-30.10.7145017.5yes
2880045.0entrepreneurmarrieduniversity.degreenoyesnocellularaprthu2.0999.00.0nonexistent-1.893.075-47.11.4105099.1no